DETECTION OF THYROID DISEASES USING MACHINE LEARNING TECHNIQUES / (Record no. 610719)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02787nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | AKHTAR,TEHSEEN |
| 245 ## - TITLE STATEMENT | |
| Title | DETECTION OF THYROID DISEASES USING MACHINE LEARNING TECHNIQUES / |
| Statement of responsibility, etc. | TEHSEEN AKHTAR |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 137p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Background: The unusual growth of the glandular tissue on the boundary of the Thyroid gland<br/>is an indication of Thyroid disease. Thyroid disease is characterised by an unusually high or<br/>low number of hormones produced by the thyroid gland, the two most prevalent kinds are<br/>hypothyroidism (underactive thyroid gland) and hyperthyroidism (overactive thyroid gland).<br/>The main aim of this project was to introduce the concept of an efficient multi-stage ensemble<br/>i.e., the voting ensemble of the homogeneous ensemble which could be used with a variety of<br/>feature-selection algorithms for improving the diagnosis of thyroid diseases. The dataset<br/>utilised in this study was built from real-time thyroid data obtained from the teaching hospital<br/>in DG Khan at District Head Quarter (DHQ), Pakistan. Following the appropriate preprocessing processes, three kinds of attribute-selection strategies were used: The first approach<br/>used was Select from Model (SFM), the second technique was the Select K-Best (SKB), and<br/>the final methodology was the Recursive Feature Elimination (RFE). Select From Model<br/>(SFM) is a form of attribute-selection strategy that uses a model to select attributes. As potential<br/>feature estimators, the Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB)<br/>and Random Forest denoted as the (RF) classifiers were employed in conjunction with each<br/>other. The homogeneous ensemble activated the bagging, boosting-based learners, who were<br/>then classified by the Voting ensemble, which employed both soft and hard voting to categorise<br/>the data. Other performance assessment criteria such as hamming loss, accuracy, mean square<br/>error, sensitivity and others have been implemented. The results of the experiments reveal that<br/>when the suggested approach for better thyroid sickness detection is applied in its most<br/>practicable form, it is most successful. On the dataset 1, all of the algorithms tested obtained<br/>100 % accuracy with subset of the total no of feature in each case, however on the dataset 2,<br/>more than 98 percent accuracy was reached in every case. On the basis of accuracy and<br/>computing cost, the results given here exceeded equivalent benchmark models in their<br/>respective fields of study. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | PhD Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Omer Gilani |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/28854">http://10.250.8.41:8080/xmlui/handle/123456789/28854</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 08/01/2024 | 629.8 | SMME-Phd-18 | Thesis |